Rolling Bearing Fault Diagnosis Based on Multi-Domain Features-Whale Optimized

Abstract

A rolling bearing fault diagnosis technique based on multi-domain feature and whale optimization algorithm-support vector machine (MDF-WOA-SVM for abbreviation) is proposed. Firstly, recursive analysis is performed on vibration signal and the recursive features are employed as nonlinear recursive feature vector including recursive rate (RR), deterministic rate (DET), recursive entropy (RE), and diagonal average length (DAL). Then, a comprehensive multi-domain feature vector is constructed by combining three time-domain features including root mean square, variance, and peak to peak. Finally, the whale optimization algorithm (abbreviated as WOA) is introduced to optimize the penalty factor C and kernel function parameter g, and the optimal WOA-SVM model is established. The rolling bearing datasets of Jiangnan University is employed for instance analysis, and the results show that the 10-CV accuracy of the technique proposed have a good performance with 99%. Compared with recursive features or time-domain features, multi-domain features are more accurate and comprehensive in describing characters of the signal. Some popular supervised learning models are also introduced for comparison including K nearest neighbor (abbreviated as KNN) and decision tree (abbreviated as DT), the result shows that the proposed method has a higher accuracy and certain advantages.

Keywords: Rolling bearing, RQA, SVM, Fault diagnosis